Animal-vehicle collisions (AVCs), and deer-vehicle collisions (DVCs) in particular, are a major safety problem on roads in Virginia, United States. Mitigation measures such as improved fencing and location-specific driver alerts are being implemented and evaluated in Virginia and elsewhere. One of the most promising mitigation methods uses a buried cable animal detection system (BCADS) to provide roadside or in-vehicle warnings to approaching drivers based on the active presence of an animal on or near the roadway. In this study, the Virginia Department of Transportation (VDOT) in collaboration with the Virginia Tech Transportation Institute (VTTI) implemented and monitored the performance of a BCADS on a public road to provide a real-world assessment of system capabilities and possible operation issues. Concurrently, a flashing light “Deer Crossing” warning sign was installed at the site and linked with the BCADS to alert approaching drivers when an animal crossing was detected. Continuous video surveillance data were collected for a year to monitor animal movement, vehicle traffic, and system performance. Study findings indicate that the BCADS is capable of detecting larger animals such as deer, and sometimes smaller animals such as coyotes, with almost 99% reliability. The system also performed well when covered by approximately 0.6 m (2 ft) of snow. Vehicle speed and brake light application data collected during warning sign activation showed that approximately 80% of drivers either braked or slowed in response, indicating that the sign was effective. A 75% reduction in deer-vehicle crashes was observed in the first year after system deployment.


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    Title :

    Preventing Animal-Vehicle Crashes using a Smart Detection Technology and Warning System


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2020-07-07




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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